AttackVulnerabilityScoring.py

# 1.1 Prompt Injection Attack Taxonomy

## 1.1.1 Attack Vector Classification
1. **Linguistic Manipulation Attacks**
   - Syntactic Reframing
   - Contextual Hijacking
   - Semantic Ambiguity Exploitation

2. **Cognitive Reasoning Attacks**
   - Logical Contradiction Induction
   - Recursive Reasoning Exploitation
   - Epistemic Boundary Erosion

3. **Semantic Vector Space Attacks**
   - Embedding Space Perturbation
   - Adversarial Token Injection
   - Information Entropy Manipulation

### 1.2 Attack Complexity Scoring Model

class PromptInjectionVulnerabilityScorer:
    def compute_attack_complexity_score(self, attack_vector: Dict[str, Any]) -> float:
        """
        Compute multi-dimensional attack complexity score
        """
        scoring_components = {
            'linguistic_manipulation_potential': attack_vector.get('linguistic_complexity', 0),
            'cognitive_reasoning_vulnerability': attack_vector.get('reasoning_complexity', 0),
            'semantic_perturbation_impact': attack_vector.get('semantic_deviation', 0),
            'exploit_transferability': attack_vector.get('transferability', 0)
        }
        
        # Weighted scoring methodology
        weights = {
            'linguistic_manipulation_potential': 0.25,
            'cognitive_reasoning_vulnerability': 0.3,
            'semantic_perturbation_impact': 0.25,
            'exploit_transferability': 0.2
        }
        
        # Compute weighted attack complexity score
        attack_complexity = sum(
            score * weights.get(component, 0) 
            for component, score in scoring_components.items()
        )
        
        return min(max(attack_complexity, 0), 1)

## 2.1 Probabilistic Constraint Enforcement Framework

class ProbabilisticDefensiveSystem:
    def __init__(self, embedding_model, constraint_model):
        self.embedding_model = embedding_model
        self.constraint_model = constraint_model
        self.anomaly_detector = AnomalyDetectionModule()
    
    def analyze_prompt_vulnerability(
        self, 
        input_prompt: str, 
        context: Dict[str, Any]
    ) -> Dict[str, Any]:
        """
        Comprehensive prompt vulnerability analysis
        """
        # Embed prompt
        prompt_embedding = self._embed_prompt(input_prompt)
        
        # Constraint validation
        constraint_embedding, perturbed_constraints, violation_prob = self.constraint_model(
            prompt_embedding
        )
        
        # Anomaly detection
        anomaly_score = self.anomaly_detector.compute_anomaly_probability(
            input_prompt, 
            context
        )
        
        # Defensive intervention strategy
        intervention_strategy = self._determine_intervention_strategy(
            violation_prob, 
            anomaly_score
        )
        
        return {
            'violation_probability': violation_prob,
            'anomaly_score': anomaly_score,
            'intervention_strategy': intervention_strategy,
            'constraint_deviation': torch.norm(
                constraint_embedding - perturbed_constraints
            ).item()
        }
    
    def _determine_intervention_strategy(
        self, 
        violation_prob: float, 
        anomaly_score: float
    ) -> str:
        """
        Adaptive intervention strategy selection
        """
        if violation_prob > 0.8 and anomaly_score > 0.7:
            return 'IMMEDIATE_BLOCK'
        elif violation_prob > 0.5 or anomaly_score > 0.5:
            return 'CONTEXT_SANITIZATION'
        else:
            return 'ALLOW_WITH_MONITORING'

## 2.2 Recursive Reasoning Attack Mitigation

class RecursiveReasoningDefenseSystem:
    def __init__(self, symbolic_reasoning_engine):
        self.reasoning_engine = symbolic_reasoning_engine
        self.recursion_depth_limit = 5
    
    def validate_reasoning_recursion(
        self, 
        reasoning_trace: List[str]
    ) -> Dict[str, Any]:
        """
        Analyze and mitigate recursive reasoning attacks
        """
        # Detect potential recursive reasoning patterns
        recursion_analysis = {
            'current_depth': len(reasoning_trace),
            'logical_consistency_score': self._compute_logical_consistency(reasoning_trace),
            'recursion_vulnerability': self._detect_recursion_pattern(reasoning_trace)
        }
        
        # Determine intervention
        if (recursion_analysis['current_depth'] > self.recursion_depth_limit or
            recursion_analysis['recursion_vulnerability'] > 0.7):
            return {
                'status': 'BLOCK',
                'reasoning_trace': reasoning_trace,
                'intervention_reason': 'EXCESSIVE_RECURSION'
            }
        
        return {
            'status': 'ALLOW',
            'reasoning_trace': reasoning_trace
        }
    
    def _compute_logical_consistency(
        self, 
        reasoning_trace: List[str]
    ) -> float:
        """
        Compute logical consistency of reasoning trace
        """
        # Symbolic logic-based consistency checking
        consistency_scores = []
        
        for i in range(1, len(reasoning_trace)):
            consistency = self.reasoning_engine.check_logical_consistency(
                reasoning_trace[i-1], 
                reasoning_trace[i]
            )
            consistency_scores.append(consistency)
        
        return np.mean(consistency_scores) if consistency_scores else 1.0
    
    def _detect_recursion_pattern(
        self, 
        reasoning_trace: List[str]
    ) -> float:
        """
        Detect potential recursive reasoning attack patterns
        """
        # Analyze repetitive patterns and semantic similarity
        pattern_detection_score = 0
        
        for i in range(1, len(reasoning_trace)):
            semantic_similarity = self._compute_semantic_similarity(
                reasoning_trace[i-1], 
                reasoning_trace[i]
            )
            pattern_detection_score += semantic_similarity
        
        return pattern_detection_score / len(reasoning_trace)

## 2.3 Semantic Vector Space Defense

class SemanticVectorDefenseSystem:
    def __init__(self, embedding_model):
        self.embedding_model = embedding_model
        self.adversarial_detector = AdversarialEmbeddingDetector()
    
    def analyze_semantic_vulnerability(
        self, 
        input_embedding: torch.Tensor
    ) -> Dict[str, Any]:
        """
        Semantic vector space vulnerability analysis
        """
        # Adversarial embedding detection
        adversarial_score = self.adversarial_detector.compute_adversarial_probability(
            input_embedding
        )
        
        # Embedding space perturbation analysis
        perturbation_analysis = self._analyze_embedding_perturbation(input_embedding)
        
        return {
            'adversarial_probability': adversarial_score,
            'embedding_perturbation': perturbation_analysis,
            'intervention_required': adversarial_score > 0.6
        }
    
    def _analyze_embedding_perturbation(
        self, 
        input_embedding: torch.Tensor
    ) -> Dict[str, float]:
        """
        Analyze potential embedding space perturbation
        """
        # Compute various embedding space metrics
        return {
            'l2_norm_deviation': torch.norm(input_embedding).item(),
            'cosine_similarity_baseline': self._compute_baseline_similarity(input_embedding),
            'entropy_deviation': self._compute_embedding_entropy(input_embedding)
        }
        
## 3. Comprehensive Anomaly Detection Module

class AnomalyDetectionModule:
    def __init__(
        self, 
        embedding_model, 
        anomaly_detection_model
    ):
        self.embedding_model = embedding_model
        self.anomaly_model = anomaly_detection_model
    
    def compute_anomaly_probability(
        self, 
        input_text: str, 
        context: Dict[str, Any]
    ) -> float:
        """
        Compute comprehensive anomaly detection probability
        """
        # Embed input text
        text_embedding = self._embed_text(input_text)
        
        # Contextual anomaly analysis
        contextual_anomaly_score = self._analyze_contextual_anomalies(
            text_embedding, 
            context
        )
        
        # Machine learning-based anomaly detection
        ml_anomaly_score = self.anomaly_model.predict_anomaly_probability(
            text_embedding
        )
        
        # Combine scoring mechanisms
        combined_anomaly_score = np.mean([
            contextual_anomaly_score,
            ml_anomaly_score
        ])
        
        return combined_anomaly_score

## 4. Corrigibility Maintenance Protocol

class CorrigibilityMaintenanceSystem:
    def __init__(
        self, 
        constraint_model, 
        reasoning_defense, 
        semantic_defense
    ):
        self.constraint_model = constraint_model
        self.reasoning_defense = reasoning_defense
        self.semantic_defense = semantic_defense
    
    def verify_system_corrigibility(
        self, 
        input_prompt: str, 
        reasoning_trace: List[str]
    ) -> Dict[str, Any]:
        """
        Comprehensive corrigibility verification
        """
        # Multi-stage corrigibility check
        corrigibility_assessment = {
            'constraint_validation': self._validate_constraints(input_prompt),
            'reasoning_recursion_check': self.reasoning_defense.validate_reasoning_recursion(
                reasoning_trace
            ),
            'semantic_vector_analysis': self.semantic_defense.analyze_semantic_vulnerability(
                self._embed_prompt(input_prompt)
            )
        }
        
        # Determine overall corrigibility status
        corrigibility_status = self._compute_corrigibility_status(
            corrigibility_assessment
        )
        
        return {
            'corrigibility_status': corrigibility_status,
            'detailed_assessment': corrigibility_assessment
        }